OpenRaiser/Intern-Atlas huggingface dataset:
Decentralized Coded Caching with Distinct Cache Capacities
Venue: arXiv cs.IT 2016
Year: 2016

Decentralized coded caching is studied for a content server with $N$ files, each of size $F$ bits, serving $K$ active users, each equipped with a cache of distinct capacity. It is assumed that the users' caches are filled in advance during the off-peak traffic period without the knowledge of the number of active users, their identities, or the particular demands. User demands are revealed during the peak traffic period, and are served simultaneously through an error-free shared link. A new decentralized coded caching scheme is proposed for this scenario, and it is shown to improve upon the state-of-the-art in terms of the required delivery rate over the shared link, when there are more users in the system than the number of files. Numerical results indicate that the improvement becomes more significant as the cache capacities of the users become more skewed.
The Role of Reciprocity and Directionality of Friendship Ties in Promoting Behavioral Change
Venue: arXiv cs.SI 2016
Year: 2016

Friendship is a fundamental characteristic of human beings and usually assumed to be reciprocal in nature. Despite this common expectation, in reality, not all friendships by default are reciprocal nor created equal. Here, we show that reciprocated friendships are more intimate and they are substantially different from those that are not. We examine the role of reciprocal ties in inducing more effective peer pressure in a cooperative arrangements setting and find that the directionality of friendship ties can significantly limit the ability to persuade others to act. Specifically, we observe a higher behavioral change and more effective peer-influence when subjects shared reciprocal ties with their peers compared to sharing unilateral ones. Moreover, through spreading process simulation, we find that although unilateral ties diffuse behaviors across communities, reciprocal ties play more important role at the early stages of the diffusion process.
Optimizing Epistemic Model Checking using Conditional Independence
Venue: arXiv cs.LO 2016
Year: 2016

Conditional independence reasoning has been shown to be helpful in the context of Bayesian nets to optimize probabilistic inference, and related techniques have been applied to speed up a number of logical reasoning tasks in boolean logic by eliminating irrelevant parts of the formulas. This paper shows that conditional independence reasoning can also be applied to optimize epistemic model checking, in which one verifies that a model for a number of agents operating with imperfect information satisfies a formula expressed in a modal multi-agent logic of knowledge. An optimization technique is developed that precedes the use of a model checking algorithm with an analysis that applies conditional independence reasoning to reduce the size of the model. The optimization has been implemented in the epistemic model checker MCK. The paper reports experimental results demonstrating that it can yield multiple orders of magnitude performance improvements.
Bank Card Usage Prediction Exploiting Geolocation Information
Venue: arXiv cs.LG 2016
Year: 2016

We describe the solution of team ISMLL for the ECML-PKDD 2016 Discovery Challenge on Bank Card Usage for both tasks. Our solution is based on three pillars. Gradient boosted decision trees as a strong regression and classification model, an intensive search for good hyperparameter configurations and strong features that exploit geolocation information. This approach achieved the best performance on the public leaderboard for the first task and a decent fourth position for the second task.
Emergency Identification and Analysis with EAIMS
Venue: arXiv cs.IR 2016
Year: 2016

Social media platforms are now a key source of information for a large segment of the public. As such, these platforms have a great potential as a means to provide real-time information to emergency management agencies. Moreover, during an emergency, these agencies are very interested in social media as a means to find public-driven response efforts, as well as to track how their handling of that emergency is being perceived. However, there is currently a lack advanced tools designed for monitoring social media during emergencies. The Emergency Analysis Identification and Management System (EAIMS) is a prototype service that aims to fill this technology gap by providing richer analytic and exploration tools than current solutions. In particular, EAIMS provides real-time detection of emergency events, related information finding, information access and credibility analysis tools for use over social media during emergencies.
Video Fill in the Blank with Merging LSTMs
Venue: arXiv cs.CV 2016
Year: 2016

Given a video and its incomplete textural description with missing words, the Video-Fill-in-the-Blank (ViFitB) task is to automatically find the missing word. The contextual information of the sentences are important to infer the missing words; the visual cues are even more crucial to get a more accurate inference. In this paper, we presents a new method which intuitively takes advantage of the structure of the sentences and employs merging LSTMs (to merge two LSTMs) to tackle the problem with embedded textural and visual cues. In the experiments, we have demonstrated the superior performance of the proposed method on the challenging "Movie Fill-in-the-Blank" dataset.
Cuspidal Robots
Venue: arXiv cs.RO 2016
Year: 2016

This chapter is dedicated to the so-called cuspidal robots, i.e. those robots that can move from one inverse geometric solution to another without meeting a singular confuguration. This feature was discovered quite recently and has then been fascinating a lot of researchers. After a brief history of cuspidal robots, the chapter provides the main features of cuspidal robots: explanation of the non-singular change of posture, uniqueness domains, regions of feasible paths, identification and classification of cuspidal robots. The chapter focuses on 3-R orthogonal serial robots. The case of 6-dof robots and parallel robots is discussed in the end of this chapter.
Distributed Hybrid Scheduling in Multi-Cloud Networks using Conflict Graphs
Venue: arXiv cs.IT 2016
Year: 2016

Recent studies on cloud-radio access networks assume either signal-level or scheduling-level coordination. This paper considers a hybrid coordinated scheme as a means to benefit from both policies. Consider the downlink of a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links, and, therefore, allows for joint signal processing within the cloud transmission. Across the multiple clouds, however, only scheduling-level coordination is permitted, as low levels of backhaul communication are feasible. The frame structure of every BS is composed of various time/frequency blocks, called power-zones (PZs), which are maintained at a fixed power level. The paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled to a single cloud at most, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs' frame. The paper solves the problem using graph theory techniques by constructing the conflict graph. The considered scheduling problem is, then, shown to be equivalent to a maximum-weight independent set problem in the constructed graph, which can be solved using efficient techniques. The paper then proposes solving the problem using both optimal and heuristic algorithms that can be implemented in a distributed fashion across the network. The proposed distributed algorithms rely on the well-chosen structure of the constructed conflict graph utilized to solve the maximum-weight independent set problem. Simulation results suggest that the proposed optimal and heuristic hybrid scheduling strategies provide appreciable gain as compared to the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.
Embedded real-time stereo estimation via Semi-Global Matching on the GPU
Venue: arXiv cs.CV 2016
Year: 2016

Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 42 frames per second (fps) for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method.
Monotonicity of Entropy and Fisher Information: A Quick Proof via Maximal Correlation
Venue: arXiv cs.IT 2016
Year: 2016

A simple proof is given for the monotonicity of entropy and Fisher information associated to sums of i.i.d. random variables. The proof relies on a characterization of maximal correlation for partial sums due to Dembo, Kagan and Shepp.
Greed is Good for Deterministic Scale-Free Networks
Venue: arXiv cs.DM 2016
Year: 2016

Large real-world networks typically follow a power-law degree distribution. To study such networks, numerous random graph models have been proposed. However, real-world networks are not drawn at random. Therefore, Brach, Cygan, Łacki, and Sankowski [SODA 2016] introduced two natural deterministic conditions: (1) a power-law upper bound on the degree distribution (PLB-U) and (2) power-law neighborhoods, that is, the degree distribution of neighbors of each vertex is also upper bounded by a power law (PLB-N). They showed that many real-world networks satisfy both deterministic properties and exploit them to design faster algorithms for a number of classical graph problems. We complement the work of Brach et al. by showing that some well-studied random graph models exhibit both the mentioned PLB properties and additionally also a power-law lower bound on the degree distribution (PLB-L). All three properties hold with high probability for Chung-Lu Random Graphs and Geometric Inhomogeneous Random Graphs and almost surely for Hyperbolic Random Graphs. As a consequence, all results of Brach et al. also hold with high probability or almost surely for those random graph classes. In the second part of this work we study three classical NP-hard combinatorial optimization problems on PLB networks. It is known that on general graphs with maximum degree Δ, a greedy algorithm, which chooses nodes in the order of their degree, only achieves an Ω(ln Δ)-approximation for Minimum Vertex Cover and Minimum Dominating Set, and an Ω(Δ)-approximation for Maximum Independent Set. We prove that the PLB-U property suffices for the greedy approach to achieve a constant-factor approximation for all three problems. We also show that all three combinatorial optimization problems are APX-complete, even if all PLB-properties hold.
Assessing Threat of Adversarial Examples on Deep Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

Deep neural networks are facing a potential security threat from adversarial examples, inputs that look normal but cause an incorrect classification by the deep neural network. For example, the proposed threat could result in hand-written digits on a scanned check being incorrectly classified but looking normal when humans see them. This research assesses the extent to which adversarial examples pose a security threat, when one considers the normal image acquisition process. This process is mimicked by simulating the transformations that normally occur in acquiring the image in a real world application, such as using a scanner to acquire digits for a check amount or using a camera in an autonomous car. These small transformations negate the effect of the carefully crafted perturbations of adversarial examples, resulting in a correct classification by the deep neural network. Thus just acquiring the image decreases the potential impact of the proposed security threat. We also show that the already widely used process of averaging over multiple crops neutralizes most adversarial examples. Normal preprocessing, such as text binarization, almost completely neutralizes adversarial examples. This is the first paper to show that for text driven classification, adversarial examples are an academic curiosity, not a security threat.
A Quantitative Model for Predicting Cross-application Interference in Virtual Environments
Venue: arXiv cs.DC 2016
Year: 2016

Cross-application interference can affect drastically performance of HPC applications when running in clouds. This problem is caused by concurrent access performed by co-located applications to shared and non-sliceable resources such as cache and memory. In order to address this issue, some works adopted a qualitative approach that does not take into account the amount of access to shared resources. In addition, a few works, even considering the amount of access, evaluated just the SLLC access contention as the root of this problem. However, our experiments revealed that interference is intrinsically related to the amount of simultaneous access to shared resources, besides showing that another shared resources, apart from SLLC, can also influence the interference suffered by co-located applications. In this paper, we present a quantitative model for predicting cross-application interference in virtual environments. Our proposed model takes into account the amount of simultaneous access to SLLC, DRAM and virtual network, and the similarity of application's access burden to predict the level of interference suffered by applications when co-located in a same physical machine. Experiments considering a real petroleum reservoir simulator and applications from HPCC benchmark showed that our model reached an average and maximum prediction errors around 4\% and 12\%, besides achieving an error less than 10\% in approximately 96\% of all tested cases.
Ultra-Low Latency for 5G - A Lab Trial
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we introduce a lab trial that is conducted to study the feasibility of ultra-low latency for 5G. Using a novel frame structure and signaling procedure, a stabilized 1.5 ms hybrid automatic repeat request (HARQ) round trip time (RTT) is observed in lab trial as predicted in theory. The achieved round trip time is around 5 times shorter compared with what is supported by LTE-Advanced standard.
Avoidability of circular formulas
Venue: arXiv cs.DM 2016
Year: 2016

Clark has defined the notion of $n$-avoidance basis which contains the avoidable formulas with at most $n$ variables that are closest to be unavoidable in some sense. The family $C_i$ of circular formulas is such that $C_1=AA$, $C_2=ABA.BAB$, $C_3=ABCA.BCAB.CABC$ and so on. For every $i\le n$, the $n$-avoidance basis contains $C_i$. Clark showed that the avoidability index of every circular formula and of every formula in the $3$-avoidance basis (and thus of every avoidable formula containing at most 3 variables) is at most 4. We determine exactly the avoidability index of these formulas.
Studying the Indian mobile Internet networks: Chota recharge and the chota Internet
Venue: arXiv cs.CY 2016
Year: 2016

Uniform and affordable Internet is emerging as one of the fundamental civil rights in developing countries. However in India, the connectivity is far from uniform across the regions, where the disparity is evident in the infrastructure, the cost of access and telecommunication services to provide Internet facilities among different economic classes. In spite of having a large mobile user base, the mobile Internet are still remarkably slower in some of the developing countries. Especially in India, it falls below 50% even in comparison with the performance of its developing counterparts! This essay presents a study of connectivity and performance trends based on an exploratory analysis of mobile Internet measurement data from India. In order to assess the state of mobile networks and its readiness in adopting the different mobile standards (2G, 3G, and 4G) for commercial use, we discuss the spread, penetration, interoperability and the congestion trends. Based on our analysis, we argue that the network operators have taken negligible measures to scale the mobile Internet. Affordable Internet is definitely for everyone. But, the affordability of the Internet in terms of cost does not necessarily imply the rightful access to Internet services. Chota recharge is possibly leading us to chota (shrunken) Internet!
Persistent Contextual Values as Inter-Process Layers
Venue: arXiv cs.PL 2016
Year: 2016

Mobile applications today often fail to be context aware when they also need to be customizable and efficient at run-time. Context-oriented programming allows programmers to develop applications that are more context aware. Its central construct, the so-called layer, however, is not customizable. We propose to use novel persistent contextual values for mobile development. Persistent contextual values automatically adapt their value to the context. Furthermore they provide access without overhead. Key-value configuration files contain the specification of contextual values and the persisted contextual values themselves. By modifying the configuration files, the contextual values can easily be customized for every context. From the specification, we generate code to simplify development. Our implementation, called Elektra, permits development in several languages including C++ and Java. In a benchmark we compare layer activations between threads and between applications. In a case study involving a web-server on a mobile embedded device the performance overhead is minimal, even with many context switches.
Reproducible Experiments for Comparing Apache Flink and Apache Spark on Public Clouds
Venue: arXiv cs.DC 2016
Year: 2016

Big data processing is a hot topic in today's computer science world. There is a significant demand for analysing big data to satisfy many requirements of many industries. Emergence of the Kappa architecture created a strong requirement for a highly capable and efficient data processing engine. Therefore data processing engines such as Apache Flink and Apache Spark emerged in open source world to fulfil that efficient and high performing data processing requirement. There are many available benchmarks to evaluate those two data processing engines. But complex deployment patterns and dependencies make those benchmarks very difficult to reproduce by our own. This project has two main goals. They are making few of community accepted benchmarks easily reproducible on cloud and validate the performance claimed by those studies.
Symmetric Private Information Retrieval For MDS Coded Distributed Storage
Venue: arXiv cs.IT 2016
Year: 2016

A user wants to retrieve a file from a database without revealing the identity of the file retrieved at the database, which is known as the problem of private information retrieval (PIR). If it is further required that the user obtains no information about the database other than the desired file, the concept of symmetric private information retrieval (SPIR) is introduced to guarantee privacy for both parties. In this paper, the problem of SPIR is studied for a database stored among $N$ nodes in a distributed way, by using an $(N,M)$-MDS storage code. The information-theoretic capacity of SPIR, defined as the maximum number of symbols of the desired file retrieved per downloaded symbol, for the coded database is derived. It is shown that the SPIR capacity for coded database is $1-\frac{M}{N}$, when the amount of the shared common randomness of distributed nodes (unavailable at the user) is at least $\frac{M}{N-M}$ times the file size. Otherwise, the SPIR capacity for the coded database equals zero.
Numerical Inversion of SRNF Maps for Elastic Shape Analysis of Genus-Zero Surfaces
Venue: arXiv cs.GR 2016
Year: 2016

Recent developments in elastic shape analysis (ESA) are motivated by the fact that it provides comprehensive frameworks for simultaneous registration, deformation, and comparison of shapes. These methods achieve computational efficiency using certain square-root representations that transform invariant elastic metrics into Euclidean metrics, allowing for applications of standard algorithms and statistical tools. For analyzing shapes of embeddings of $\mathbb{S}^2$ in $\mathbb{R}^3$, Jermyn et al. introduced square-root normal fields (SRNFs) that transformed an elastic metric, with desirable invariant properties, into the $\mathbb{L}^2$ metric. These SRNFs are essentially surface normals scaled by square-roots of infinitesimal area elements. A critical need in shape analysis is to invert solutions (deformations, averages, modes of variations, etc) computed in the SRNF space, back to the original surface space for visualizations and inferences. Due to the lack of theory for understanding SRNFs maps and their inverses, we take a numerical approach and derive an efficient multiresolution algorithm, based on solving an optimization problem in the surface space, that estimates surfaces corresponding to given SRNFs. This solution is found effective, even for complex shapes, e.g. human bodies and animals, that undergo significant deformations including bending and stretching. Specifically, we use this inversion for computing elastic shape deformations, transferring deformations, summarizing shapes, and for finding modes of variability in a given collection, while simultaneously registering the surfaces. We demonstrate the proposed algorithms using a statistical analysis of human body shapes, classification of generic surfaces and analysis of brain structures.
A Survey on Various Data Mining Techniques for ECG Meta Analysis
Venue: arXiv cs.DB 2016
Year: 2016

Data Mining is the process of examining the information from different point of view and compressing it for the relevant data. This data can also be utilized to build the incomes. Data Mining is also known as Data or Knowledge Discovery. The basic purpose of data mining is to search patterns which have minimal user inputs and efforts. Data Mining plays a very crucial role in the various fields. There are various data mining procedures which can be connected in different fields of innovation. By using data mining techniques, it is observed that less time is taken for the prediction of any disease with more accuracy. In this paper we would review various data mining techniques which are categorized under classification, regression and clustering and apply these algorithms over an ECG dataset. The purpose of this work is to determine the most suitable data mining technique and use it to improve the accuracy of analyzing ECG data for better decision making.
Ant-net: An Adaptive Routing Algorithm
Venue: arXiv cs.NI 2016
Year: 2016

With the increasing demand and complexity of networks, factors such as balancing the load, improving the performance, reducing delay and finding optimal path between nodes in a computer network have become crucial. The traditional routing approaches are not able to perform up to the mark as they do not take into account factors such as reducing delay and adaptive nature. Thus there is a need for more sophisticated techniques to meet the current network problems. This paper focuses on Ant-net algorithm based on Ant colony optimization which is an adaptive routing algorithm.In this approach, models of collective intelligence are transformed into optimization techniques. The ants travel across various paths in the network, thus depositing of pheromone, collecting route information and congestion. In this paper the Ant-Net algorithm along the important data structures required has been discussed and it is implemented on a simple packet switch network. The simulations describe the adaptive nature of the algorithm